An image-based decision system processes and extracts information from an image or multiple images to make decisions such as the presence of objects of interest, disease, defects, or the acceptance of measurement parameters such as dimensions, intensity, structures, etc. Image-based decision systems have broad applications such as machine vision, non-contact gauging, inspection, robot guidance, medical imaging, biometrics, etc.
Many image-based decision functions involve the detection of defects or gauging of dimensions from man-made manufacturing components, parts or systems. Simple filtering, thresholding, template matching, golden template comparison and caliper based edge detection are the primary prior art approaches for performing simple machine vision inspection and measurement tasks (Silver, B, “Geometric Pattern Matching for General-Purpose Inspection in Industrial Machine Vision”, Intelligent Vision '99 Conference—Jun. 28–29, 1999).
There is often a teaching phase and an application phase for an image-based decision system. In the prior art approach, template region(s) or golden template(s) are selected by human and stored in the system in the teaching phase. In addition, edge detection calipers are specified at image regions of interest for edge detection through multiple one-dimensional projection and simple differentiation filters. In the application phase, template searches are applied to locate the template region(s) in the input image. The located template locations are used to establish a reference coordinate system and/or for deriving points and structures for measurements. Edges are detected from each caliper region and/or Golden template is subtracted from the normalized input image for defect detection or dimensional measurements (Hanks, J, “Basic Functions Ease Entry Into Machine Vision”, Test & Measurement World, Mar. 1, 2000 Titus, J, “Software makes machine vision easier”, Test & Measurement World, Oct. 15, 2001
Increasing quality and precision requirement in advanced manufacturing demands that quality control procedures be implemented at every stage of the manufacturing process. This requires advanced inspection applications to be deployed in the factory floor by users who have little or no knowledge of image processing/pattern recognition/machine vision technology. Simple prior art algorithms cannot properly address these requirements. There are growing demands of solution products for image-based decisions. A solution product requires little technical knowledge of its users to fully utilize its capabilities. It should allow users to use their application domain knowledge (not detailed image processing and pattern recognition technical knowledge) to optimize the results. Therefore, a solution product should take inputs that link to application knowledge and automatically or semi-automatically translate them into detailed technical processing procedure and parameters without user interaction.
For simple applications, the prior art teaching process requires human selection of template region(s) and the selection and specification of edge calipers and thresholds for measurements. For advanced applications, non-standard processing algorithms are often required using a collection of basic image processing functions. This is mostly performed by experienced image processing and pattern recognition personnel in an ad-hoc fashion. The teaching process is time-consuming and involves mostly artistic trial-and-error rather than a systematic engineering approach. The effectivity of the resulting inspection process is highly dependent on the experience level of the person who sets up the process. This is inconsistent and expensive. Furthermore, the resulting processing sequences and the effects and side-effects of individual operations may not be easily comprehendible. Many operations may be closely linked. Change of one operation may require associated changes of other operations to achieve the effect of change. This requires advanced expert knowledge. The processing sequence may not correlate well to the application requirement or data representation. Therefore, they are difficult to update, change, or debug.